Sunday, December 17, 2017

10 uses for Chatbots in learning (with examples)

As chatbots become common in other contexts, such as retail,
health and finance, so they will become common in learning. Education is always
somewhat behind other sectors in considering and adopting technology but adopt
it will. There are several points across the learner journey where bots are
already being used and already a range of fascinating examples.

1.Onboarding bot

Onboarding
is notoriously fickle. New starters in at different times, have different needs
and the old model of a huge dump of knowledge, documents and compliance courses
is still all too common. Bots are being used to introduce new students or staff
to the people, environment and purpose of the organisation. New starters have predictable
questions, so answers can be provided straight to mobile, directed to people,
processes or procedures, where necessary. It is not that the chatbot will
provide the entire solution but it will take the pressure off and respond to
real queries as they arise. Available 24/7 it can give access to answer as well
as people. What better way to present your organization as innovative and
responsive to the needs of students and staff?

2.FAQ bot

In a sense
Google is a chatbot. You type something in and up pops a set of ranked links.
Increasingly you may even have a short list of more detailed questions you may
want to ask. Straight up FAQ chatbots, with a well-defined set of answers to a
predictable set of questions can take the load off customer queries, support
desks or learner requests. A lot of teaching is admin and a chatbot can relieve
that pressure at a very simple level within a definite domain – frequently
asked questions.

3. Invisible LMS bot

At another
level, the invisible LMS, fronted by a chatbot, allows people to ask for help
and shifts formal courses into performance support, within the workflow.
LearningPool’s ‘Otto’ is a good example. It sits on top of content, accessible
from Facebook, Slack and other commonly used social tools. You get help in
various forms, such as simple text, chunks of learning, people to contact and
links to external resources as and when you need them. Content is no longer
sits in a dead repository, waiting on you to sign in or take courses, but is a
dynamic resource, available when you ask it something.

4. Learner engagement bot

Learners
are often lazy. Students leave essays and assignments to the last minute,
learners fail to do pre-work, and courses– it’s a human failing. They need
prompting and cajoling. Learner engagement bots do this, with pushed prompts to
students and responses to their queries. ‘Differ’ from Norway does precisely
this. It recognizes that learners need to be engaged and helped, even pushed
through the learning journey, and that is precisely what 'Differ' does.

5. Learner support bot

Campus
support bots or course support bots go one stage further and provide teaching
support in some detail. The idea is to take the administrative load off the
shoulders of teachers and trainers. Response times to emails from faculty to
students can be glacial. Learner support bots can, if trained well, respond
with accurate and consistent answers quickly, 24/7.

The Georgia
Tech bot Jill Watson, and its descendants, responds in seconds. Indeed they had
to slow its response time down to mimic the typing speed of a human. The
learners, 350 AI students, didn’t guess that it was a bot and even put it up
for a teaching award.

6. Tutor bots

Tutor bots are different from chatbots in terms of the
goals, which are explicitly ‘learning’ goals. They retain the qualities of a
chatbot, flowing dialogue, tone of voice, exchange and human (like) but focus
on the teaching of knowledge and skills.Straight up teaching is another
approach, where the bot behaves like a Socratic teacher, asking sprints of
questions and providing encouragement and feedback. This type of bot can be
used as a supplement to existing courses to encourage engagement. Wildfire, the
AI content generation service uses bots of this type to deliver actual teaching on apprenticeship content, as a supplement to courses, also built using AI, in
minutes not months. Once the basic knowledge has been acquired, the bot tests
the student as well as getting them to apply their knowledge.

7. Mentor bot

The point
of a bot may not be to simply answer questions but to mentor learners by
providing advice on how to find the information on your own, to promote problem
solving. AutoMentor by Roger Schank,is
one such system, where the bot knows the context and provides, not just FAQ
answers but advice. Providing answers is not always the best way to teach. At a
higher-level chatbots could be used to encourage problem solving and critical
skills, by being truly Socratic, acting as a midwife to the students behaviours
and thoughts. Roger Schank is using these in defence-funded projects on Cyber
Security.

As the
dialogue gets better, drawing not only on a solid knowledge-base, good learner
engagement through dialogue, focused and detailed feedback but also critical
thought in terms of opening up perspectives, encouraging questioning of
assumptions, veracity of sources and other aspects of perspectival thought, so
critical thinking could also be possible. Bots will be able to analyse text to
expose factual, structural or logical weaknesses. The absence of critical
thought will be identified as well as suggestions for improving this skill by
prompting further research ideas, sound sources and other avenues of thought.
This ‘bot as critical companion’ is an interesting line of development.

8. Scenario-based bots

Beyond
knowledge, we have the teaching and learning of more sophisticated scenarios,
where knowledge can be applied. This is often absent in education, where almost
all the effort is put into knowledge acquisition. It is easy to see why – it’s
hard and time consuming. Bots can set up problems, prompt through a process,
provide feedback and assess effort. Scenarios often involve other people this
is where surrogate bots can come in.

9. Practice bots

Practice
bots, literally take the role of a customer, patient, learners or any other
person and allows learners to practice their customer care, support, healthcare
or other soft skills on a responding person (bot). Bots that act as revision
bots for exams are also possible.

A bot that
mimics someone can be used for practice. For example, the boy with attitude
‘Eli’, developed by Penn State, that mimics an awkward child in the classroom.
It is used by student teachers to practice their skills on dealing with such
problems before they hit the classroom. Duolingo uses bots after you have
gathered an adequate vocabulary, knowledge of grammar and basic competence, to
allow practice in a language. This surely makes sense.

10. Wellbeing bots

If a bot is being used in any therapeutic context, its
anonymity can be an advantage. From Eliza in the 60s to contemporary therapeutic
bots, this has been a rich vein of bot development. There is an example of the word
‘suicidal’ appearing in a student messenger dialogue, that led to a fast
intervention, as the student was in real distress. Therapeutic bots are being
used in controlled studies to see of they have a beneficial effect on outcomes.
Anonymity, in itself, is an advantage in such bots, as the learner may not want
to expose their failings.

Bots such as ‘Elli ‘ and ‘Woebot’ are already being
subjected to controlled trials to examine the impact on clinical outcomes.

Bot warning

The holy
grail in AI is to find generic algorithms that can be used (especially in
machine learning) to solve a range of different problems across a number of
different domains. This is starting to happen with deep learning (machine learning).
The idea is that the teacher bot will replace the skills of a teacher, not just
be able to tutor in one subject alone, but be a cross-curricular teacher,
especially at the higher levels of learning. It could be cross-departmental,
cross-subject and cross-cultural, to produce teaching and learning that will be
free from the tyranny of the institution, department, subject or culture in
which it is bound. Let’s be clear, this
will not happen any time soon.AI is
nowhere near solving the complex problems that this entails. If someone is
promising a bot will replace a teacher – show them the door. Bots will augment
not automate teaching.

We have to
be careful about overreach here. Effective bots are not easy to build, have to
be ‘trained (in AI-speak ‘unsupervised’) and are difficult to build. On the
other hand trained bots, with good data sets (in AI-speak ‘supervised’), in
specific domains, are eminently possible. Another warning is that they are on a
collision course with traditional Learning Management Systems, as they usually
need a dynamic server-side infrastructure. As for SCORM – the sooner it’s
binned the better. Bots fit n more naturally into the xAPI landscape.

Conclusion

Chatbots have real potential in a number of learning
activities, all along the learning journey, not as a general; ‘teacher’ but in
specific applications within specific domains. They need to be trained, built,
tested and improved, which is no easy task, but their efficacy in reducing the
workload of teachers, trainers, lecturers and administrators is clear. The
dramatic advances in Natural Language Processing have led to Siri, Amazon Echo
and Google Home. It is a rapidly developing field of AI and promises to deliver
chatbot technology that is better and cheaper by the month.

As a bot
does not have the limitations of a human, in terms of forgetting, recall, cognitive
bias, cognitive overload, getting ill, sleeping 8 hours a day, retiring and
dying - once on the way to acquiring, albeit limited, skills, it will only get
better and better. The more students that use its service the better it gets,
not only on what it teaches but how it teaches. Courses will be fine-tuned to
eliminate weaknesses, and finesse themselves to produce better outcomes.

We have
seen how online behaviour has moved from flat page-turning (websites) to
posting (Facebook, Twitter) to messaging (Txting, Messenger). We have seen how
the web become more natural and human. As interfaces (using AI) have become
more frictionless and invisible, conforming to our natural form of
communication (dialogue), through text or speech. The web has become more
human.

Learning
takes effort. Personalised dialogue reframes learning as an exploratory, yet
still structured process where the teacher guides and the learner has to make
the effort. Taking the friction and cognitive load of the interface out of the
equation, means the teacher and learner can focus on the task and effort needed
to acquire knowledge and skills. This is the promise of bots. But the process
of adoption will be gradual.

Finally,
this at last is a form of technology that teachers can appreciate, as it truly
tries to improve on what they already do. It takes good teaching as its
standard and tries to support and streamline it to produce faster and better
outcomes at a lower cost. It takes the admin and pain out of teaching. They are
here, more are coming.